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v0.2.0: visualizations, interactive browser, arXiv paper, gap analysis

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New features:
- 12 interactive visualizations (ietf viz): t-SNE landscape, similarity
  heatmap, score distributions, timeline, bubble explorer, radar charts,
  author network graph, category treemap, quality vs overlap, org bar chart,
  ideas chart, and interactive draft browser
- Interactive draft browser (browser.html): filterable by category, keyword,
  score sliders with sortable table and expandable detail rows
- arXiv paper (paper/main.tex): 13-page manuscript with all findings
- Gap analysis: 12 identified under-addressed areas
- Author network: collaboration graph, org contributions, cross-org analysis
- Draft generation from gaps (ietf draft-gen)
- Auto-load .env for API keys (python-dotenv)

New modules: visualize.py, authors.py, draftgen.py
New reports: timeline, overlap-matrix, authors, gaps
New deps: plotly, matplotlib, seaborn, scipy, scikit-learn, networkx, python-dotenv

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Chris Nennemann
2026-02-28 13:37:55 +01:00
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# IETF Draft Analyzer
Track, categorize, rate, and visualize AI/agent-related IETF Internet-Drafts.
**260 drafts** analyzed across **19 categories** with **403 authors**, **1,262 extracted ideas**, and **12 identified gaps** — spanning June 2025 to February 2026.
## What This Does
The IETF is experiencing an unprecedented wave of standardization activity around AI agents. This tool provides a quantitative lens on that activity:
- **Fetches** draft metadata and full text from the IETF Datatracker API
- **Rates** each draft on 5 dimensions (novelty, maturity, overlap, momentum, relevance) using Claude
- **Embeds** drafts with Ollama for pairwise similarity and clustering
- **Extracts** discrete technical ideas and identifies landscape gaps
- **Maps** the author collaboration network and organizational affiliations
- **Generates** interactive visualizations, markdown reports, and a filterable browser
- **Produces** publication-ready figures for an arXiv paper
## Quick Start
```bash
# Install
pip install -e .
# Set your API key (or add to .env file)
export ANTHROPIC_API_KEY=sk-ant-...
# Fetch drafts from IETF Datatracker
ietf fetch
# Rate all unrated drafts with Claude
ietf analyze --all
# Generate embeddings (requires Ollama running locally)
ietf embed
# Extract technical ideas
ietf ideas --all
# Identify gaps in the landscape
ietf gaps
# Generate all visualizations
ietf viz all
# Open the interactive browser
xdg-open data/figures/browser.html
```
## CLI Commands
### Core Pipeline
| Command | Description |
|---------|-------------|
| `ietf fetch` | Fetch AI/agent drafts from IETF Datatracker |
| `ietf analyze --all` | Rate all unrated drafts using Claude (5 dimensions + summary) |
| `ietf embed` | Generate semantic embeddings via Ollama |
| `ietf ideas --all` | Extract technical ideas from drafts using Claude |
| `ietf gaps` | Identify under-addressed areas in the landscape |
| `ietf authors --fetch` | Fetch author/affiliation data from Datatracker |
### Exploration
| Command | Description |
|---------|-------------|
| `ietf list` | List tracked drafts |
| `ietf show <name>` | Show detailed info for a specific draft |
| `ietf search <query>` | Full-text search across all stored drafts |
| `ietf similar <name>` | Find the most similar drafts by embedding similarity |
| `ietf clusters` | Find clusters of near-duplicate drafts |
| `ietf compare <name1> <name2> ...` | Compare drafts for overlap and unique contributions |
| `ietf authors` | Show top authors and their draft counts |
| `ietf network` | Show organizational collaboration network |
### Visualizations (`ietf viz`)
All outputs go to `data/figures/`. Interactive charts are standalone HTML files (no server needed).
| Command | Output | Format |
|---------|--------|--------|
| `ietf viz all` | Generate everything below | mixed |
| `ietf viz browser` | Filterable draft browser with search, category chips, score sliders | HTML |
| `ietf viz landscape` | t-SNE/UMAP 2D scatter of all drafts colored by category | HTML |
| `ietf viz heatmap` | 260x260 clustered pairwise similarity matrix | PNG |
| `ietf viz distributions` | Violin plots for all 5 rating dimensions by category | PNG |
| `ietf viz timeline` | Stacked area chart of monthly submissions by category | HTML |
| `ietf viz bubble` | Novelty vs Maturity explorer (size=relevance, color=category) | HTML |
| `ietf viz radar` | Average rating profile per category | HTML |
| `ietf viz network` | Author co-authorship force-directed graph | HTML |
| `ietf viz treemap` | Category composition treemap (color=avg score) | HTML |
| `ietf viz quality` | Score vs uniqueness with quadrant annotations | HTML |
| `ietf viz orgs` | Organization contribution horizontal bar chart | HTML |
| `ietf viz ideas` | Ideas frequency by type | HTML |
### Reports (`ietf report`)
Markdown reports saved to `data/reports/`.
| Command | Description |
|---------|-------------|
| `ietf report overview` | Sortable table of all rated drafts with bar-chart scores |
| `ietf report landscape` | Category-grouped view with per-category rankings |
| `ietf report timeline` | Monthly submission volume and category trends |
| `ietf report overlap-matrix` | Top similar pairs, per-category overlap, cross-category matrix |
| `ietf report authors` | Top authors, organizations, collaboration pairs |
| `ietf report digest` | Weekly digest of recently fetched drafts |
| `ietf report ideas` | Most common ideas, unique ideas, ideas by type |
### Other
| Command | Description |
|---------|-------------|
| `ietf draft-gen <topic>` | Generate an Internet-Draft addressing a landscape gap |
| `ietf config` | Show or modify configuration |
## Rating System
Each draft is scored 1-5 on five dimensions:
| Dimension | What it measures |
|-----------|-----------------|
| **Novelty** | Originality relative to existing standards |
| **Maturity** | Completeness of specification |
| **Overlap** | Redundancy with other drafts (5 = heavily overlapping) |
| **Momentum** | Community engagement, revisions, adoption |
| **Relevance** | Importance to the AI/agent ecosystem |
**Composite score:**
```
score = 0.30 * novelty + 0.25 * relevance + 0.20 * maturity + 0.15 * momentum + 0.10 * (6 - overlap)
```
## Key Findings
- **36x growth** in 9 months (2 drafts/month to 72)
- **7.9% of draft pairs** exceed 0.80 cosine similarity — significant redundancy
- **Safety deficit**: AI safety proposals (36) are vastly outnumbered by protocol proposals (290+)
- **Organizational concentration**: Top 5 orgs contribute ~35% of all drafts
- **1,262 technical ideas** extracted across 6 types (mechanism, architecture, protocol, pattern, extension, requirement)
- **12 identified gaps** in the current landscape
## Tech Stack
- **Python 3.11+** with Click CLI
- **SQLite** with FTS5 full-text search and WAL mode
- **Anthropic Claude** (Sonnet 4) for analysis, rating, idea extraction, gap analysis
- **Ollama** (nomic-embed-text) for local embeddings and similarity
- **Plotly** for interactive HTML visualizations
- **Matplotlib/Seaborn** for publication-ready static figures
- **NetworkX** for author collaboration graph analysis
- **NumPy/SciPy/scikit-learn** for similarity computation and dimensionality reduction
## Project Structure
```
src/ietf_analyzer/
cli.py # Click CLI entry point (all commands)
fetcher.py # IETF Datatracker API client
analyzer.py # Claude-based analysis, rating, idea extraction, gap analysis
embeddings.py # Ollama embeddings + cosine similarity + clustering
db.py # SQLite with FTS5 (7 tables: drafts, ratings, embeddings, llm_cache, authors, draft_authors, ideas, gaps)
models.py # Author, Draft, Rating dataclasses
reports.py # Markdown report generation
visualize.py # Interactive HTML + static PNG visualizations
authors.py # AuthorNetwork: Datatracker author fetching, collaboration graph
draftgen.py # Internet-Draft generation from gap analysis
config.py # Configuration with defaults
data/
drafts.db # SQLite database (all analysis data)
reports/ # Generated markdown reports
figures/ # Generated visualizations (HTML + PNG)
paper/
main.tex # arXiv paper: "The AI Agent Standardization Wave"
export_figures.py # Export interactive charts to static images
Makefile # Build: make pdf
```
## Database Schema
| Table | Purpose | Records |
|-------|---------|--------:|
| `drafts` | Draft metadata + full text | 260 |
| `ratings` | 5-dimension AI ratings + summary | 260 |
| `embeddings` | Semantic vectors (nomic-embed-text) | 260 |
| `llm_cache` | Claude API response cache | ~500 |
| `authors` | Person records from Datatracker | 403 |
| `draft_authors` | Author-draft relationships | 742 |
| `ideas` | Extracted technical ideas | 1,262 |
| `gaps` | Gap analysis results | 12 |
| `drafts_fts` | FTS5 full-text search index | — |
## arXiv Paper
A 13-page paper is included in `paper/main.tex`:
> **The AI Agent Standardization Wave: A Quantitative Analysis of 260 IETF Internet-Drafts on Autonomous Agents and Artificial Intelligence**
Build with:
```bash
cd paper
python3 export_figures.py # copy/export figures
pdflatex main.tex # compile (run twice for references)
```
## Configuration
```bash
# Show current config
ietf config
# Change Claude model
ietf config --set claude_model claude-sonnet-4-20250514
# API key via .env file (auto-loaded)
echo "ANTHROPIC_API_KEY=sk-ant-..." > .env
```
## Cost
Full analysis of 260 drafts consumed ~475K API tokens (rating + idea extraction + gap analysis). At current Sonnet pricing, this is approximately $2-3 USD.
## License
MIT